> whoami()

> whoami()

> whoami()

> whoami()

> whoami()

> demo()

> demo()

> demo()

> demo()

> demo()

> demo()

> demo()

> demo()

> demo()

> example(production)

• 2006: calling R scripts from PHP (both reading from MySQL) to generate custom plots embedded in a homepage
• 2008: automated/batch R scripts to generate thousands of pages of crosstables, ANOVA and plots from SPSS with pdflatex
• 2011: Ruby on Rails web application calling RApache and pandoc to generate reports in plain English (NoSQL databases, vertical scaling, security, central error tracking etc)
• 2012: plain RApache web application for NLP and network analysis
• 2015: standardizing the data infrastructure of a fintech startup to use R both in bath jobs and stream processing (ETL, reporting, fraud detection, daily operations, customer communication etc)
• 2017: redesign, monitor and scale the DS infrastructure of an adtech startup for batch training and live scoring

> production <<- list(…)

Using in R in a non-interactive way:

• R scripts are scheduled to run without manual intervention (eg CRON or upstream job trigger, API request)
• need for a standard, eg containerized environment (eg R and package versions, OS packages, .Rprofile etc)
• security! (safeguarded production environment, SQL injection, AppArmor, encrypted credentials etc)
• the output of the jobs are recorded and monitored (eg error handler for ErrBit, CloudWatch logs or Splunk etc), alerts and notifications
• if an error occurs, usually there is no other way to figure out what happened then looking at the recorded job output, so better log than sorry

TODO

TODO